Step 1 Check codebook for skip patterns

Check the codebook and the
appendix containing the data collection forms in the Plan and Operations Report
to determine if a skip pattern affects the variables in your analysis. The
Plan and Operation Report link is the first bullet under Data and
Documentation/Codebook Files heading on the NHANES II page. See the
Locate Variables module Task 1 for more information on how to locate background
information on variables in the documentation.

Skip
Patterns in Blood Pressure Questionnaire in Appendix of Plan and Operation
Report

Step 2 Check data for skip patterns

After you have used the codebook to discover
if a skip pattern affects variables in your analysis, you will use cross
tabulations obtained by the SAS proc freq procedure to determine the
effect of skip patterns.

Program to Check Data for Skip Patterns

Statements

Explanation

Procfreqdata =demo2_nh2;

Use the proc freq procedure to determine the
frequency of each value of the variables listed.

where n2ah0047>=20 ;

Use the where statement to select participants
who were age 20 years and older.

Use the table statement to list the variables to be
included in the output frequency table and the cross
tabulation frequency table for the skip patterns. Use the
list missing option to display
missing values. Note that
a star (*) indicates that a crosstab will be constructed
with n2ah1059 as the row variable and n2ah1060
as the column variable. The syntax for a cross-tabulation is
row variable(s)*column variable(s) and designates that the
variable listed before the star will be the row variable and
the variable listed after the star will be the column
variable.

Highlighted items from the proc freq
output for skip patterns:

Note that in the cross-tabulation of n2ah1059 by n2ah1060 that
there are 10,745 observations where the response is "no"
to both questions on
diagnosis of hypertension.

Note that in the cross-tabulation of n2ah1059 by n2ah1060 that
there are 4,302 observations where the response to n2ah1059 is "yes"
and
n2ah1060 is missing and 294 observations where the response to n2ah1059 is
"no"
and n2ah1060 is "yes."
These responses will need to be recoded, or
a new variable created, in order to estimate the total percent of
persons who have diagnosed hypertension.

Further down, the output includes a cross tabulation of n2ah1060 by
n2ah1067, n2ah1068, and n2ah1069. Note that there are exactly
10,745
observations where the response to n2ah1060 is "no"
and the response is
missing for n2ah1067, n2ah1068, and n2ah1069. These respondents were not
asked these questions because of a skip pattern.

Use the proc freq procedure to determine the
frequency of each value of the variables listed; use the
data statement to refer to your analytic dataset; use
the where statement to select participants who were
age 20 years and older (n2ah0047>=20);
use the table statement to indicate variables of
interest for the output.

Use the proc freq and table statements
to check the derived variable (diagHTN) against the
original variables (n2ah1059 and
n2ah1060); use the
data statement to refer to your analytic dataset; use
the where statement to select participants who were
age 20 years and older (n2ah0047>=20);
use the table statement to indicate variables of
interest for the output.

Highlighted items from the recode output for
skip patterns:

Options 1 and 2 produce the same results: 10,761 respondents are coded
as "2,"for the
derived variable, diagHTN.